CN111079860B - Method for constructing multi-granularity response model of emergency based on scene similarity - Google Patents
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Abstract
The invention discloses a method for constructing a multi-granularity response model of an emergency based on scene similarity, which comprises the steps of collecting multi-source intelligence data related to the emergency as an input variable of the model; extracting a key scene unit, and decomposing scene elements of the key scene unit; forming a training sample set of the SVM; extracting the characteristic attributes of the emergency, calculating the structural similarity and attribute similarity of the target scene and the historical scene, and obtaining the comprehensive similarity of the target scene and the historical scene; and introducing a spatial granularity risk index calculation formula, and comparing the urgency degree of the node variable of the scene unit at the next moment to determine a key node. The invention combines a test design method with an agent model, supports a high-level decision user to make an emergency scheme, meets the requirement of a low-level user on focusing event evolution key node, reduces the calculation workload of optimization design, realizes global optimal analysis and improves the reliability of the system.
Description
Technical Field
The invention relates to a response model of an urban flood disaster emergency, in particular to a construction method of a multi-granularity response model of the emergency with scene similarity.
Background
The emergency response of urban emergency needs to quickly determine the grade of the event, accurately generate an effective decision scheme and track the change of the situation in real time. Due to the situation emergency and the limitation of information collection time, the related emergency decision information often shows the characteristics of difficult quantization and fuzziness. At present, most of researches on emergency decision-making of emergencies by scholars at home and abroad are focused on introducing a mathematical model into a decision-making process or considering the psychological behaviors of the scholars and determining an optimal emergency decision-making scheme by combining historical case information. The method considers psychological characteristics of sensitivity, loss avoidance and the like of decision makers equally, and provides an emergency scheme optimizing method aiming at casualties and property loss under different scenes. And the Yuan and the like calculate the comprehensive perception value of each response scheme under the epidemic outbreak by considering the psychological behavior of the regret of a decision maker. From the perspective of a plurality of decision-making subjects, the Wangbang and the like describe the relationship among different attribute values, different decision-making schemes and different situation states by applying a Bayesian decision network in order to reduce the risk of an emergency. Managing information fusion problems of artificial emergency decision layers such as cloud and the like provides a new idea, a distance entropy concept is provided, and a knowledge element theory is introduced to obtain a fusion set. Based on emergency response research of historical cases, certain limitations exist in structural feature description and real-time evolution analysis in a specific process of an emergency, and more scholars pay attention to situational representation of the emergency. The Wangyanzhu, Wangning and other people introduce the sense of the situation on the basis of the common knowledge meta-model and divide the situation of the emergency case data. The mid-autumn wild goose and the like perform instantiation constraint research on the scene meta model, the scene conceptual model and the scene model. And the similarity test is carried out on the characteristic attributes of the scene elements and the emergency events at a new visual angle of information perception by people such as Yangpeng and the like, so that the situation perception of the emergency events is realized. Chenxuolong and the like perform scene element formalization representation on the emergency, and discuss a generalization algorithm for mapping low-level scenes to high-level scenes on the basis of particle calculation.
The emergency response research of the emergency is not limited to a certain state any more, and the occurrence, development and evolution processes of the emergency need to be controlled integrally. The scenario deduction rules and the evolution algorithm of the emergency are mature, but few scholars refine the scenario of the emergency according to the decision requirements of users at different levels, and how to accurately and effectively transmit the decision information to the specific implementation level of the basic level after the high level of strategic decision makes a response still need to be continuously researched.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the problems of various flooding disaster information complexity, decision-making demand information confusion and weak pertinence of response measures in urban flooding disaster emergencies, introduces a support vector machine and a similarity algorithm, constructs a dynamic response model fusing scenes based on a granularity principle, and realizes scene refinement from a decision-making high layer to a decision-making low layer.
The technical scheme is as follows:
the invention discloses a method for constructing an emergency multi-granularity response model based on scene similarity, which comprises the following steps:
(1) collecting multisource intelligence data related to the emergency as an input variable of the model;
(2) extracting key scene units of urban flood disaster emergencies, and carrying out scene element decomposition on the key scene units;
(3) selecting indexes according to attribute variables in the scene knowledge elements to form a training sample set of the SVM; realizing the setting of support vector machine parameters and the construction of a model by means of an R language; inputting index data to be subjected to hierarchical prediction into an SVM classification function, and performing classification learning to obtain the risk level of the emergency;
(4) extracting the characteristic attributes of the emergency by taking the scene elements as basic units, and calculating the structural similarity and the attribute similarity of the target scene and the historical scene to obtain the comprehensive similarity of the target scene and the historical scene;
(5) and according to the mapping relation of attribute evolution in different scene units, on the basis of a scene knowledge element network, refining the scene units, introducing a spatial granularity risk index calculation formula, and comparing the urgency degree of the node variable of the scene unit at the next moment to determine a key node.
The invention achieves the following beneficial effects:
the invention is based on the idea of multi-granularity, fine-grained emergency situation units are used for constructing a multi-granularity response model meeting the decision-making requirements of users at different levels, solves the problems of various flood disaster information complexity, disordered decision-making requirement information and poor response measure pertinence existing in urban flood disaster emergency, introduces a support vector machine and a similarity algorithm, constructs a dynamic response model fusing scenes based on the granularity principle, defines key nodes of event evolution, realizes scene refinement from high-level decision to low-level decision, and provides support for efficiently responding to the emergency.
Drawings
Fig. 1 is a structural diagram of an emergency multi-granularity response model based on context similarity according to an embodiment of the present invention.
Detailed Description
In order to improve the response capability of an emergency, and to effectively support the requirement of multi-level users for responding to the emergency as much as possible on the basis of fully considering the current situation and the actual requirements of emergency treatment departments of the emergency, the invention discloses a method for constructing a multi-granularity response model of the emergency with scene similarity, which is shown in fig. 1 and comprises the following steps:
(1) and collecting multi-source information data related to the emergency, such as decision-making subject information, service data, dynamic data which changes and updates in real time in the evolution process of the emergency, related field knowledge and the like, as input variables of the model.
(2) Knowledge element representation of a scene unit.
Key scenario unit S for extracting urban flood disaster emergency i Represented by formula (1). E is an event set, C is a disaster carrier set, Y is an emergency activity set, and T is a time set of occurrence of the key scene unit. Key scene unit S i Performing scene element decomposition, and dividing into input variable set I (I) 1 ，I 2 ，...，I n ) Set of state variables G (G) 1 ，G 2 ，...，G n ) Output variable set O (O) 1 ，0 2 ，...，O n ) Wherein n represents the number of elements. The input variables, state variables, and output variables in the scene unit are described in the attribute knowledge meta-model specification, and expressed as expression (2).
Si＝(E，C，Y，T) (1)
Wherein, the first and the second end of the pipe are connected with each other,
qualitative or quantitative characterization of attribute variables，
A measurement dimension representing a variable of the attribute,
representing the evolution trend of the attribute variables.
(3) And constructing an SVM classification function.
According to the attribute variable e in the scene knowledge element k And selecting indexes to form a training sample set of the SVM. And realizing the setting of parameters of the support vector machine and the construction of the model by means of the R language. There are three types of support vector machine classifiers that can be selected: c-classification, nu-classification, and one-classification. Meanwhile, there are four types of kernel functions that can be selected: linear kernel functions (linear), polynomial kernel functions (polynomial), radial basis kernel functions (RBF), and neural network kernel functions (sigmoid). The classifier type and the kernel function are combined in sequence, and finally the combination with the optimal judgment result is selected through comparison, so that the proportion of indexes can be adjusted, and the prediction precision is improved. And (3) selecting attribute variables subjected to the normalized description of the knowledge element model according to a formula (2), inputting the attribute variables into an SVM classification function, performing classification learning to obtain a classification result, and providing decision situation data for responding to the development of the overall control event of the user at the strategic decision high level.
(4) And calculating the comprehensive similarity of the target scene and the historical scene.
The scene element is taken as a basic unit, and the characteristic attributes of the emergency are extracted from three aspects of an event object, a disaster carrier and emergency activities. Calculating the structural similarity phi (A, B) of the target scene and the historical scene according to the formula (3), wherein A is a non-empty attribute set of the target scene, B is a non-empty attribute set of the historical scene, W represents the weight of the attributes in the scene, and W represents the weight of the attributes in the scene A∩B Sum of attribute weights, W, representing the intersection of non-empty attributes of the target scene and the historical scene t And the sum of the attribute weights of the non-empty attribute union representing the target scene and the historical scene.
When the characteristic attribute of the emergency is an accurate number:
wherein A is f Is the value of the target scene attribute f, B if Is the value of the attribute f of the ith scene in the history scene, f max And f min Respectively representing the maximum value and the minimum value of the value range of the attribute f.
When the characteristic attribute of the emergency is the interval number:
where m is a definite number attribute value, [ m1, m2 ]]，[n1，n2]Are interval number attributes, and are all larger than f min Is less than f max 。
When the characteristic attribute of the emergency is a linguistic variable, the characteristic attribute needs to be expressed as a triangular fuzzy number (k) firstly ij ，l ij ，p ij ) And then normalized to (theta 1, theta 2, theta 3), wherein alpha is a benefit-type attribute and beta is a cost-type attribute.
Finally, (theta 1, theta 2, theta 3) is converted into interval numbers [ rho 1, rho 2 ]]，
Calculating comprehensive similarity:
wherein the content of the first and second substances,
for structural similarity, R i (A, B) is the attribute similarity of the ith attribute, w i Is the weight of the ith attribute.
(5) And (4) calculating a risk index at spatial granularity.
After a new scenario unit is induced and generated by the output variable of the previous scenario unit, the input variable of the new scenario unit influences the internal state, the disaster-bearing carrier of the new scenario unit is granulated into a finer-level place set, and the risk degrees of different places are different, namely the risk degree values of the node variables are calculated under the space granularity. The disaster carrier site set of the new scenario unit is represented as: p ═ P 1 ，P 2 ，...，P n }
P is P n Probability of internal state deterioration, E denotes P n Number of sudden events occurred, w n Weight, U, representing the nth factor n Representing the environmental impact factor of an incident at the nth spatial granularity, C j Indicating an emergency factor at the jth spatial granularity. Selecting U ═ U here 1 ，U 2 ，U 3 ，U 4 ，U 5 }，U 1 Is the area value of a certain spatial granularity, U 2 For population numbers at this spatial granularity, U 3 Number of residential housing at this spatial granularity, U 4 Is the number of enterprises, U, at this spatial granularity 5 The average income of people under the space granularity; c ═ C 1 ，C 2 ，U 3 }，C 1 The number of emergency agencies under a certain spatial granularity, including government offices, police teams, police stations, etc., C 2 For the number of medical institutions at this spatial granularity, including hospitals, clinics, pharmacies, etc., C 3 And the infrastructure at the space granularity is disaster-resistant.
Description of specific embodiments: the technical scheme of the invention is applied to urban flood disaster emergencies, and the specific implementation mode takes '6.27 rainstorm events in Changzhou city of Jiangsu province in 2015' as an example to verify the effectiveness of the method for constructing the multi-granularity response model of the emergencies based on the scene similarity. According to the annual water conservancy work summary published by the water conservancy bureau of the Changzhou city, the rainfall value of the 6.27 rainstorm incident of the Changzhou city of Jiangsu province in 2015, the submerged water depth, duration, the number of the population suffering from a disaster, the number of the population dying, the number of house collapse, the flooded area of crops, the direct economic loss value, the reservoir operation condition, the collapsed condition of the polder area, the construction of a drainage system, the percentage of the greening area, the population density, the disaster resistance of the infrastructure and the response level are extracted. Through the steps (1) to (5) of the specific implementation mode, the risk level of the urban flood disaster emergency, the emergency scheme of the similar historical situation and the risk degree value of the node variable can be obtained. The technical scheme provided by the invention can effectively support the decision requirements of multi-level users for responding to emergencies, the demand levels are divided from the strategic decision high level to the tactical execution basic level, and the generated multi-granularity response model not only supports the high-level users to make emergency schemes, but also meets the requirements of low-level users for focusing the most urgent place, and provides situation information of granularity suitable for the decision-makers at different levels. The problems of various flood disaster information complexity, disordered decision-making demand information and weak response measure pertinence existing in urban flood disaster emergencies are solved.
Claims (6)
1. A method for constructing an emergency multi-granularity response model based on scene similarity is characterized by comprising the following steps:
(1) collecting multisource intelligence data related to the emergency as an input variable of a model;
(2) extracting key scene units of urban flood disaster emergencies, and carrying out scene element decomposition on the key scene units;
(3) selecting indexes according to attribute variables in the scene knowledge elements to form a training sample set of the SVM; realizing the setting of support vector machine parameters and the construction of a model by means of an R language; inputting index data to be subjected to hierarchical prediction into an SVM classification function, and performing classification learning to obtain the risk level of the emergency;
(4) extracting the characteristic attributes of the emergency by taking the scene elements as basic units, and calculating the structural similarity and the attribute similarity of the target scene and the historical scene to obtain the comprehensive similarity of the target scene and the historical scene;
(5) according to the mapping relation of attribute evolution in different scene units, on the basis of a scene knowledge element network, the scene units are refined, a spatial granularity risk index calculation formula is introduced, the degree of urgency of node variables of the scene units at the next moment is compared, and key nodes are determined;
in the step (4), the structural similarity between the target scene and the historical scene is calculated by adopting the formula (3)
Wherein A is a non-empty attribute set of the target scene, B is a non-empty attribute set of the history scene, W represents the weight of the attribute in the scene, W A∩B Sum of attribute weights, W, representing the intersection of non-empty attributes of the target scene and the historical scene t Attribute weight summation of non-empty attribute union set representing target scene and historical scene;
calculating the comprehensive similarity:
wherein the content of the first and second substances,
for structural similarity, R i (A, B) is the attribute similarity of the ith attribute, w i Is the weight of the ith attribute.
2. The method for constructing a multi-granularity response model of emergency based on scene similarity as claimed in claim 1, wherein in step (1), the collected multi-source intelligence data comprises decision-making subject information, service data and dynamic data which changes and updates in real time in the evolution process of the emergency.
3. The method for constructing the multi-granularity emergency response model based on the scene similarity as claimed in claim 1, wherein in the step (2), the key scene unit S i Represented by the formula (1),
Si＝(E,C,Y,T) (1)
in the formula, E is an event set, C is a disaster carrier set, Y is an emergency activity set, and T is a time set in which a key scenario unit appears.
4. The method for constructing an emergency multi-granularity response model based on scene similarity as claimed in claim 3, wherein in step (2), a key scene unit S is used i Performing scene element decomposition, and dividing into input variable set I (I) 1 ，I 2 ，…，I n ) Set of state variables G (G) 1 ，G 2 ，…，G n ) Output variable set O (O) 1 ，0 2 ，…，0 n ) Wherein n represents the number of elements.
5. The method as claimed in claim 3, wherein in step (2), the input variables, the state variables and the output variables in the scene units are described by the attribute meta-knowledge model specification, which is expressed as formula (2),
wherein, the first and the second end of the pipe are connected with each other,
a qualitative or quantitative characterization representing a property variable,
a measurement dimension representing a variable of the attribute,
representing the evolution trend of the attribute variables.
6. The method for constructing the multi-granularity response model of the emergency based on the scene similarity as claimed in claim 1, wherein in the step (4), the characteristic attributes of the emergency are extracted from three aspects of an event object, a disaster carrier and emergency activities.
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